The invention relates generally to monitoring devices and equipment for obtaining and illustrating data about a patient to which the equipment is connected and more particularly to monitoring devices and methods for triggering alarms based on the data from the patient.
In monitoring or diagnostic devices that are currently utilized, the data obtained by the devices is compared to a set limit for the particular physiologic parameter being measured and represented by the incoming data to the device. When the data exceeds the limit, the device is configured to trigger or set off an alarm in order to indicate the current condition of the patient to a treating physician or other medical care professional that is monitoring the patient.
However, in many situations the limits for the triggering of the alarms are set close to the ranges of normal fluctuations of the values for the parameters being monitored, providing a safety net to prevent adverse events from being missed. As such, even when the parameter value only drops below the alarm limit for an instant due to a non-critical event, the device will trigger an alarm based on that sensed value. While setting the alarm limit in this manner is a safeguard against any significant issue or clinically relevant alarm 1002 being missed, the low alarm limit for the monitored parameter also creates a large number of clinically irrelevant alarms 1000, as illustrated in FIG. 3. In FIG. 3, with the alarm limit for a pulse oximetry value detected by a suitable monitoring, device set at 90%, the monitoring device will clearly catch any adverse event that occurs when the event causes the pulse oximetry value for the patient to drop below this threshold.
However, as a result of the closeness of the threshold or alarm limit to the normal or acceptable ranges for this parameter, a large number of clinically irrelevant alarms are generated as well. Further, it is not possible to differentiate the clinically relevant alarms from the clinically irrelevant alarms based on the parameter value alone, such that each alarm event must be acted on in the same manner by the medical personnel monitoring the patient.
One result of the large number of the clinically irrelevant alarm events is the unnecessary expenditure of personnel, time and resources in attending to the clinically irrelevant alarm events. Another result is that certain highly important clinical events could inadvertently be overlooked or missed amidst the normally much larger number of clinically irrelevant alarm events. This is often referred to as alarm fatigue and results from the constant representation of the alarm events in a similar manner that can cause certain events to become “lost” in the flood of alarms and associated information represented on the display screen of the particular device.
As current physiologic limit alarms have a very high false positive rate and setting the limits wider can reduce false positives but at the risk of missing critical events, this risk can be mitigated by the use of analytics in the device to detect patient deterioration (e.g. respiratory depression) which provides more actionable alerts as well as a safety net. In a prior attempt to address this issue, devices have been designed that include analytic engines or analytics that use specific algorithms to correlate multiple sensed parameters from a single patient. This correlation can be utilized to relate the different parameters in a manner that enables the device to predict the adverse event(s) that trigger the alarm based on the trends in the incoming data regarding the sensed parameters.
One particular embodiment of such a system is disclosed in co-pending and co-owned U.S. Non-Provisional patent application Ser. No. 14/101,663, which is expressly incorporated by reference herein in its entirety. As shown in FIG. 2, this analytic uses the respiration rate (RR) and pulse oximetry (SpO2) parameters in a suitable algorithm to predict pending patient respiratory distress in a patient.
However, while this can be a very useful analytic system for predicting respiratory distress events, without changes in the physiological alarm limits for the device the overall alarm burden is not decreased. In particular, looking back at FIG. 3, without any changes to the alarm limits for the parameters used by the analytics, with the SpO2 low default limit 1006 at 90% there still will be a significant number of clinically irrelevant limit alarms 1000 regardless of the determinations/predictions of the adverse events 1004 made by the analytics. Further, in the absence of any additional information, it is difficult to separate clinically relevant alarms 1002 from the irrelevant alarms 1000. This would also be true for other analytics and other parameters used in those analytics, e.g., a hemodynamic analytic based on blood pressure (BP) and pulse rate (PR) or other parameter combinations utilized in or by the algorithms employed in analytics.
Therefore, in order to address alarm fatigue and reduce the number of clinically irrelevant alarms that are generated, it is desirable to develop a monitoring device and associated analytic system that operates or can be configured with variable physiological parameter limits. Such a device and system would allow for the effective determination of an adverse event has occurred with the patient using the analytics, while also allowing for variations in the monitored parameters to limit and/or reduce the number of alarms being triggered for clinically irrelevant events.